Methods and apparatus for adaptable presentation of sensor data

ABSTRACT

A client device includes a display and a processor configured to obtain a data stream from a remote sensor via a communication protocol. The remote sensor is a physiological sensor monitoring a subject and/or an environmental sensor monitoring an environment in a vicinity of the subject, and the data stream includes a sensor metric, a metric identifier, and dynamically updated integrity information about the sensor metric. The processor is also configured to identify a statistical distribution function associated with the remote sensor via a function selector associated with the communication protocol, and display, via the display, the sensor metric with statistical information about the sensor metric using the identified statistical distribution function.

RELATED APPLICATIONS

This application is a continuation application of pending U.S. patentapplication Ser. No. 16/060,178, filed Jun. 7, 2018, which is a 35U.S.C. § 371 national stage application of PCT Application No.PCT/US2016/065742, filed on Dec. 9, 2016, which itself claims thebenefit of and priority to U.S. Provisional Patent Application No.62/266,196 filed Dec. 11, 2015, the disclosures of which areincorporated herein by reference as if set forth in their entireties.The above-referenced PCT International Application was published in theEnglish language as International Publication No. WO 2017/100519 A1 onJun. 15, 2017.

FIELD OF THE INVENTION

The present invention relates generally to monitoring devices andmethods, more particularly, to monitoring devices and methods formeasuring physiological information.

BACKGROUND OF THE INVENTION

FIG. 1 illustrates a conventional way of displaying biometricinformation 10 from a biometric sensor via a display 11 of a mobileelectronic device 12, such as a smartphone, that is in communicationwith the biometric sensor. The biometric sensor that generates the datadisplayed in FIG. 1 is a blood pressure sensor that sends blood pressureinformation 12 and heart rate information 14 to an application (or“mobile app”) running on the mobile electronic device for display.However, currently there is no mechanism by which a person can judge theaccuracy of the displayed sensor information 10.

For example, if the mobile app connects with a first blood pressuresensor on one day and then connects with a second, different bloodpressure sensor the another day, the person may get the same bloodpressure readings from each blood pressure sensor. However, each bloodpressure sensor may have a different accuracy, precision and probabilitydistribution. For example, the first blood pressure sensor may have astandard deviation (σ) of 6 and the second blood pressure sensor mayhave a standard deviation (σ) of 9. Thus, even if the readings of bothblood pressure sensors are the same, the person may not be able to trustthe readings of the second sensor as much as those of the first sensor.To complicate matters further, the difference in accuracy or precisionbetween the first and second blood pressure sensors may further dependon not just the sensor differences themselves, but also on static (orquasi-static) biometric data from the person being monitored, such asage, ethnicity, height, weight, gender, medication usage, health status,etc.

Moreover, when a mobile app is used to make a medical assessment or toplan an intervention or therapy, such as recommending a medicalscreening or suggesting blood pressure medication, there may be no goodway to assess the probability of false alarms. Because the precision ofeach sensor may be different, the false positives, false negatives, truepositives and true negatives in making a health assessment (such as ahypertension assessment, cardiovascular assessment, or the like) may bedifferent for each sensor.

Thus, there is increased concern in the medical community as standardsfor wireless sensors, such as BLE (Bluetooth Low energy) standards,enable any biometric sensor to send biometric information becausecurrently all sensors are mistakenly presumed to have equal statisticalcharacteristics when making a medical assessment via a mobileapplication.

SUMMARY

It should be appreciated that this Summary is provided to introduce aselection of concepts in a simplified form, the concepts being furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of thisdisclosure, nor is it intended to limit the scope of the invention.

According to some embodiments of the present invention, a method ofpresenting data from a sensor that is monitoring a subject and/or anenvironment in a vicinity of the subject includes displaying a sensormetric simultaneously with sensor performance information via a displayof an electronic device, such as a smartphone or other client device,that is in communication with the sensor. The term “client device”, asused herein, refers to a device that is separated by function and/orphysical location from the sensor device, but is in wired or wirelesscommunication with the sensor device. Exemplary sensor performanceinformation includes information about the accuracy of the sensor and/orsensor measurement statistics. In some embodiments, the sensorperformance information may be displayed as at least one probabilitydistribution curve with the sensor data. The sensor performanceinformation may be obtained from the sensor, from data storage, and/orfrom another source.

According to some embodiments of the present invention, a method ofpresenting physiological information via a display of an electronicdevice, such as a smartphone or other client device, includes receivingsensor data at the electronic device from a physiological sensor incommunication with the electronic device, and then displaying the sensordata simultaneously with sensor performance information via the display.Exemplary physiological sensors include, but are not limited to PPG(photoplethysmography) sensors, blood pressure sensors, etc. Thephysiological sensor may be in wireless communication with theelectronic device in some embodiments.

Exemplary sensor performance information includes information about theaccuracy of the physiological sensor and/or sensor measurementstatistics. In some embodiments, the sensor performance information maybe displayed as at least one probability distribution curve with thesensor data. The sensor performance information may be obtained from thephysiological sensor, from data storage, and/or from another source.

According to other embodiments of the present invention, a systemincludes a sensor configured to sense physiological information from asubject, and a signal processor configured to process signals from thesensor into a serial data stream of physiological information and sensorperformance information. An electronic device having a display isconfigured to receive the serial data stream and display thephysiological information simultaneously with the sensor performanceinformation via the display. Exemplary sensor performance informationincludes information about the accuracy of the physiological sensorand/or sensor measurement statistics. In some embodiments, the signalprocessor is configured to process signals from the sensor into a serialdata stream of physiological information, sensor performance informationand sensor measurement statistics.

The electronic device may be a mobile communication device, such as asmartphone, and may be configured to receive the serial data stream fromthe physiological sensor wirelessly. In some embodiments the sensorperformance information may be displayed as at least one probabilitydistribution curve with the sensor data.

According to other embodiments of the present invention, a systemincludes a sensor configured to sense environmental information and asignal processor configured to process signals from the sensor into aserial data stream of environmental information and sensor performanceinformation. An electronic device having a display, such as a smartphoneor other portable device, is configured to receive the serial datastream and display the environmental information simultaneously with thesensor performance information via the display.

It is noted that aspects of the invention described with respect to oneembodiment may be incorporated in a different embodiment although notspecifically described relative thereto. That is, all embodiments and/orfeatures of any embodiment can be combined in any way and/orcombination. Applicant reserves the right to change any originally filedclaim or file any new claim accordingly, including the right to be ableto amend any originally filed claim to depend from and/or incorporateany feature of any other claim although not originally claimed in thatmanner. These and other objects and/or aspects of the present inventionare explained in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which form a part of the specification,illustrate various embodiments of the present invention. The drawingsand description together serve to fully explain embodiments of thepresent invention.

FIG. 1 illustrates a conventional display of biometric information to aperson via a display of a mobile electronic device, such as asmartphone.

FIGS. 2 and 3 are probability plots for systolic and diastolic bloodpressure sensors, respectively, and that can be displayed via a displayof a mobile electronic device.

FIGS. 4-6 illustrate the presentation of biometric information andinformation about the integrity of the biometric information to a user,for example via a display of a mobile electronic device according tosome embodiments of the present invention.

FIGS. 7A-7B illustrate serial data streams that include biometricinformation, information about the integrity of the biometricinformation, and other statistical information, according to someembodiments of the present invention.

FIG. 8 is a block diagram of a system for implementing embodiments ofthe present invention.

FIG. 9 illustrates a display of data to a user by a sensor systemconfigured to generate a diagnostic (yes/no) assessment of a healthcondition, according to some embodiments of the present invention.

FIG. 10 illustrates a communication protocol presented as a series ofSensorEvents, according to some embodiments of the present invention.

DETAILED DESCRIPTION

The present invention will now be described more fully hereinafter withreference to the accompanying figures, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein. Like numbers refer to like elementsthroughout. In the figures, certain components or features may beexaggerated for clarity, and broken lines illustrate optional featuresor operations unless specified otherwise. In addition, the sequence ofoperations (or steps) is not limited to the order presented in thefigures and/or claims unless specifically indicated otherwise. Featuresdescribed with respect to one figure or embodiment can be associatedwith another embodiment or figure although not specifically described orshown as such.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

When an element is referred to as being “connected”, “coupled”,“responsive”, or variants thereof to another element, it can be directlyconnected, coupled, or responsive to the other element or interveningelements may be present. In contrast, when an element is referred to asbeing “directly connected”, “directly coupled”, “directly responsive”,or variants thereof to another element, there are no interveningelements present. Like numbers refer to like elements throughout.Furthermore, “coupled”, “connected”, “responsive”, or variants thereofas used herein may include wirelessly coupled, connected, or responsive.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Well-known functions or constructions may not be described indetail for brevity and/or clarity. The term “and/or” includes any andall combinations of one or more of the associated listed items.

As used herein, the terms “comprise”, “comprising”, “comprises”,“include”, “including”, “includes”, “have”, “has”, “having”, or variantsthereof are open-ended, and include one or more stated features,integers, elements, steps, components or functions but does not precludethe presence or addition of one or more other features, integers,elements, steps, components, functions or groups thereof. Furthermore,as used herein, the common abbreviation “e.g.”, which derives from theLatin phrase “exempli gratia,” may be used to introduce or specify ageneral example or examples of a previously mentioned item, and is notintended to be limiting of such item. The common abbreviation “i.e.”,which derives from the Latin phrase “id est,” may be used to specify aparticular item from a more general recitation.

It will be understood that although the terms first, second, third, etc.may be used herein to describe various elements/operations, theseelements/operations should not be limited by these terms. These termsare only used to distinguish one element/operation from anotherelement/operation. Thus a first element/operation in some embodimentscould be termed a second element/operation in other embodiments withoutdeparting from the teachings of present inventive concepts. The samereference numerals or the same reference designators denote the same orsimilar elements throughout the specification.

The term “about”, as used herein with respect to a value or number,means that the value or number can vary by +/−twenty percent (20%).

The term “remote”, as used herein, does not necessarily mean that aremote device is a wireless device or that it is a long distance awayfrom a device in communication therewith. Rather, the term “remote” isintended to reference a device or system that is distinct from anotherdevice or system or that is not substantially reliant on another deviceor system for core functionality. For example, a computer wired to awearable device may be considered a remote device, as the two devicesare distinct and/or not substantially reliant on each other for corefunctionality. Notwithstanding the foregoing, any wireless device (suchas a portable device, for example) or system (such as a remote databasefor example) is considered remote to any other wireless device orsystem.

The terms “respiration rate” and “breathing rate”, as used herein, areinterchangeable.

The terms “heart rate” and “pulse rate”, as used herein, areinterchangeable.

The terms “sensor”, “sensing element”, and “sensor module”, as usedherein, are interchangeable and refer to a sensor element or group ofsensor elements that may be utilized to sense information, such asinformation (e.g., physiological information, body motion, etc.) fromthe body of a subject and/or environmental information in a vicinity ofthe subject. A sensor/sensing element/sensor module may comprise one ormore of the following: a detector element, an emitter element, aprocessing element, optics, mechanical support, supporting circuitry,and the like. Both a single sensor element and a collection of sensorelements may be considered a sensor, a sensing element, or a sensormodule. A sensor/sensing element/sensor module may be configured to bothsense information and process that information into one or more metrics.

The term “monitoring” refers to the act of measuring, quantifying,qualifying, estimating, sensing, calculating, interpolating,extrapolating, inferring, deducing, or any combination of these actions.More generally, “monitoring” refers to a way of getting information viaone or more sensing elements. For example, “blood health monitoring”includes monitoring blood gas levels, blood hydration, andmetabolite/electrolyte levels.

The term “physiological” refers to matter or energy of or from the bodyof a creature (e.g., humans, animals, etc.). In embodiments of thepresent invention, the term “physiological” is intended to be usedbroadly, covering both physical and psychological matter and energy ofor from the body of a creature.

The term “body” refers to the body of a subject (human or animal) thatmay wear or otherwise be attached to a monitoring device or sensor,according to embodiments of the present invention.

As used herein, the term “processor” broadly refers to a signalprocessing circuit or computing system, or processing or computingmethod, which may be localized and/or distributed. For example, alocalized signal processing circuit may comprise one or more signalprocessing circuits or processing methods localized to a generallocation, such as to an activity monitoring device. Examples of suchdevices may comprise, but are not limited to, an earpiece, a headpiece,a finger clip, a toe clip, a limb band (such as an arm band or legband), an ankle band, a wrist band, a nose band, a sensor patch, apparel(clothing) or the like. Examples of a distributed processing circuitinclude “the cloud,” the internet, a remote database, a remote processorcomputer, a plurality of remote processing circuits or computers incommunication with each other, etc., or processing methods distributedamong one or more of these elements. The difference between distributedand localized processing circuits is that a distributed processingcircuit may include delocalized elements, whereas a localized processingcircuit may work independently of a distributed processing system.Microprocessors, microcontrollers, or digital signal processing circuitsrepresent a few non-limiting examples of signal processing circuits thatmay be found in a localized and/or distributed system.

The terms “mobile application”, “mobile app” and “app”, as used herein,are interchangeable and refer to a software program that can run on acomputing apparatus, such as a mobile phone, digital computer,smartphone, database, cloud server, processor, wearable device, or thelike.

The term “health”, as used herein, is broadly construed to relate to thephysiological status of an organism or of a physiological element orprocess of an organism. For example, cardiovascular health may refer tothe overall condition of the cardiovascular system, and a cardiovascularhealth assessment may refer to an estimate of blood pressure, VO₂max,cardiac efficiency, heart rate recovery, arterial blockage, arrhythmia,atrial fibrillation, or the like. A “fitness” assessment is a subset ofa health assessment, where the fitness assessment refers to how one'shealth affects one's performance at an activity. For example, a VO₂maxtest can be used to provide a health assessment of one's mortality or afitness assessment of one's ability to utilize oxygen during anexercise.

The term “blood pressure”, as used herein, refers to a measurement orestimate of the pressure associated with blood flow of a person.

The term “limits of agreement” (LOA), as used herein, refers to thelimits of agreement between a sensor and a benchmark, typically usingBland-Altman formalism. For example, 95% LOA refers to a range of±1.96*σ about the mean difference between a sensor and a benchmark,where σ=the standard deviation of the sensor with respect to thebenchmark. Approximately 95% of all data points collected by a sensorwill fall between ±1.96*σ of this mean difference. Thus a heart ratesensor estimate of 100 BPM (beats per minute), where the heart ratesensor is characterized by a σ=3 BPM and a mean difference of zero (allwith respect to a benchmark), would indicate that the true heart ratecould be between approximately 106 and 94 BPM, with approximately 95%certainty or higher.

The term “metric” generally refers to a measurement or measurementsystem of a property, and a “sensor metric” refers to a measurement ormeasurement system associated with a sensor. The metric may comprise anidentifier for a type of measurement, a value of the measurement, and/ora diagnosis based on the measurement. For example, a metric may comprise“blood pressure”, with a value of “120/80”, and/or a diagnosis of“normal”.

The term “metric integrity”, as used herein, refers to informationrelating how well a sensor, or a processor associated with a sensor, isable to dynamically track (in real time) the real value of a metric,based on prior statistical analysis of the sensor against a knownbenchmark. For example, the signal-to-noise (S/N) ratio of a signal canbe analyzed by a processor and/or circuit and this information can beplaced into a serial data stream, along with other sensor information,such that a mobile app can determine whether the sensor readings aretruly tracking a metric, with metric integrity changing dynamically inreal-time. In some embodiments of the present invention, metricintegrity information may comprise numerical information on the“confidence” that the sensor readings are correct, wherein a higherconfidence reading implies a higher confidence that the sensor isgenerating physiologically correct information as opposed to unwantednoise information (such as unwanted motion-, electrical-, orenvironmental-artifacts). An example of a system and method forgenerating S/N ratios for metric integrity (or signal “confidence”) in awearable PPG sensor module is presented in U.S. Patent ApplicationPublication No. 2016/0094899, which is incorporated herein by referencein its entirety.

The term “metric statistic”, as used herein, refers to static orquasi-static statistical information relating to the statisticallyvalidated performance of a sensor in a controlled benchmark studyagainst a known benchmark sensor configured to sense the same metric.Examples of metric statistics may include, but are not limited to:standard deviation (σ), limits of agreement (LOA), R² coefficient,variance, % error, receiver-operating characteristic (ROC) curveinformation, and the like. As a specific example, blood pressure sensordata output may include information on the LOA with a known benchmark,for example: LOA=±10 mmHg. While both metric integrity and metricstatistics are based on statistical information, metric statistics aretypically static or quasi-static and do not change during measurements,whereas metric integrity may change continuously with changing noiseconditions in real-time. In the case where a sensor is configured togenerate a diagnosis, such as a binary yes/no diagnosis of a healthcondition, rather than generating a metric value from a broad range ofpossible values, the metric statistics for that sensor may bebetter-described by diagnostic sensitivity/specificity analysis, withestimates for sensor accuracy, false positives, false negatives, truepositives, true negatives, F1, markedness, informedness, false negativerate, false positive rate, Mathew's Correlation Coefficient, and thelike. Thus, the metric statistics presented in a data stream maycomprise information about the diagnostic sensitivity/selectivitycharacteristics.

The term “sensor performance information”, as used herein, refers toinformation about at least one functional characteristic of a sensor,configured to provide sensed information that can be used to generate ametric, such as a physiological, environmental, or physical activitymetric. Examples of functional characteristics of a sensor include, butare not limited to, a metric statistic and/or metric integrityassociated with the sensor.

It should be noted that the terms “confidence” and “signal quality” mayoften be used interchangeably herein. However, there are some slightdifferences between these two terms. Whereas “signal quality” relatesprimarily to the signal-to-noise ratio of a sensor reading, “confidence”relates primarily to an assessment based on “signal quality”. Forexample, a high signal quality of a given threshold value may correspondwith a 100% confidence threshold, such that sensor readings or sensorbiometrics associated with signal qualities higher than the thresholdvalue may be assumed to be accurate with a probability of 100%. Thehigher the signal quality, the higher one can trust a sensed metric, andthus the higher the metric integrity.

FIGS. 2 and 3 illustrate probability plots 20, 30 for systolic anddiastolic blood pressure sensors, respectively. Two types of sensorprobability distributions are shown for each plot: 1) PPG(photoplethysmography-based blood pressure sensor), and 2) cuff(cuff-based blood pressure sensor). The PPG-based blood pressure sensorhas a broader probability distribution (higher standard deviation a)than the cuff-based sensor. For example, if a person tested their bloodpressure several times with both the PPG sensor and the cuff sensor, themean blood pressures may be about the same, such as 100 mmHg, asillustrated in FIG. 2. However, the dispersion of readings over time maybe broader for the PPG sensor, i.e., the variance (σ²) of the Gaussiandistribution may be higher for the PPG sensor. The 95% limits ofagreement (LOA) for the PPG sensor would be ±1.96*σ_(PPG)≅±18 mmHg whenestimating systolic blood pressure, whereas the 95% LOA for the cuffwould only be ±1.96*σ_(cuff)≅12 mmHg. Thus, for the PPG sensorassociated with FIG. 2, a random systolic blood pressure reading of 100mmHg on the PPG sensor means there is a reasonable chance that the truesystolic blood pressure is as high as 118 mmHg and as low as 82 mmHg. Incontrast, the dispersion of possibilities for the cuff sensor is muchlower. For example, as illustrated, true systolic blood pressure may beas high as 112 mmHg and as low as 88 mmHg. FIG. 3 presents similarinformation for diastolic readings from the same PPG sensor and bloodpressure cuff sensor of FIG. 2, but with different calculations for the95% LOA due to different values of a between the systolic and diastolicmeasurements.

Conventional mobile applications presenting data from a PPG sensorand/or a cuff sensor are not configured to present dispersioninformation to a user. For example, if a wireless (i.e. Bluetooth, WiFi,Zigbee, ANT+, etc.) PPG sensor and cuff sensor were both sending adiastolic reading of 70 mmHg to a mobile application (such as asmartphone or mobile device application), then the user may not be ableto ascertain how well they can trust that measurement, or how manymeasurements they should make in order to arrive at the same averageblood pressure measurement with each sensor. In contrast, embodiments ofthe present invention provide a meaningful way of presenting dispersioninformation to a user or someone monitoring the user (such as a medicalpractitioner). As such, embodiments of the present invention facilitateproviding medical assessments (such as a hypertension assessment or thelike) in context with false positives, false negatives, true positives,and true negatives. As such embodiments of the present invention areadvantageous in that they may help improve the efficiency of medicaltriage and may help prevent the over- or under-prescribing ofmedications when a subject is using a mobile application for medicalassessments or treatments.

FIG. 4 illustrates a presentation 40 of biometric information from aphysiological sensor (in this case blood pressure sensor), according tosome embodiments of the present invention. The presentation 40 may bevia a display of a mobile electronic device, for example, such asdisplay 98 of client device 96 in FIG. 8. The presentation 40 allows theuser and/or someone monitoring the user to assess the accuracy orurgency of the biometric assessment, e.g., a health assessment, fitnessassessment, etc. Although systolic and diastolic blood pressure areillustrated in FIG. 4, it is understood that many other physiologicalmetrics may be presented in accordance with embodiments of the presentinvention, such as heart rate, respiration rate and other breathingcharacteristics (e.g., breathing volume, peak breath velocity, breathingirregularities, and the like), cardiac output, blood pressure (e.g.,systolic, diastolic, and mean pressure), blood oxygen, blood hydrationstatus, and the like. Moreover, embodiments of the present invention maybe used with physical activity metrics associated with the body or apart of the body as well, such as speed, distance traveled, physicalposition, physical location, user cadence (i.e., of an activity),rotational speed, acceleration, or the like. Such physiological andactivity metrics may be sensed using various body-worn sensors that arewell-known to those skilled in the art. Embodiments of the presentinvention are not limited to the presentation of blood pressureinformation and blood pressure sensor information.

Biometric information and sensor information can be presented in variousways in accordance with embodiments of the present invention. Ingeneral, at least one axis of a displayed graph is related to the sensormetric of interest and at least one other axis is related to aprobability characteristic (i.e., such as a probability distribution orother function of probability) associated with that sensor metric. Inthe illustrated embodiment, of FIG. 4, blood pressure is plotted alongthe x-axis, and probability distribution is plotted along the y-axis.However, embodiments of the present invention are not limited to theillustrated presentation 40 of FIG. 4. For example, the x-axis andy-axis in FIG. 4 can be switched such that blood pressure (or anothersensor metric) is plotted along the y-axis and the probabilitydistribution is plotted along the x-axis. Moreover, alternativegraphical representations may be used.

In the illustrated embodiment of FIG. 4, the presentation 40 shows therespective probability distributions for both systolic and diastolicpressure estimates centered around the estimated pressure data taken forone measurement. In this particular case, both the systolic anddiastolic readings have come from a PPG-based sensor as described inU.S. Pat. Nos. 8,251,903, 8,647,270, and 8,700,111, the disclosures ofwhich are incorporated herein by reference in their entireties. Thiswould not necessarily imply that the estimated values presented would bethe true values or the expected values of systolic or diastolic pressurefor this instance in time, or that averaging multiple measurements withthe subject at static blood pressure would result in the estimatedvalues shown as being the expected value of the probability distributionfor this instance in time. Rather, FIG. 4 presents the known statisticaldistributions of the blood pressure sensor centered around the estimatedDP (diastolic pressure) and SP (systolic pressure) values from a singlemeasurement, as though that single measurement were the mean or expectedvalue of the distribution at a particular instance in time. Thepresentation 40 allows the user to have a basic understanding of theaccuracy and/or precision of the measurement in view.

For example, the user will know that there is a 95% chance that theexpected value (the true blood pressure value) is within about two (˜2)standard deviations of the estimated number. An alternate methodologywould be to only plot out the presentation 40 of FIG. 4 after multipleestimates of blood pressure had been generated, such that a true meanvalue can be calculated and plotted at the center of distribution. Inpractice, it may be best to generate this plot only after astatistically appropriate number of measurements have been generated.For example, a sensor characterized by a high correlation coefficient(R²) with respect to a benchmark sensor may require fewer measurementsfor averaging to calculate and present the true center of thedistribution to a user. However, it should be noted that taking multiplemeasurements to generate the true center of the distribution may requirethat the user be at a static blood pressure over the measurementcollection period of time, and it may be impractical to assume that thesubject's blood pressure would not change over that period of time.

In the illustrated embodiment of FIG. 3, it should be noted that the PPGsensor demonstrated≅0 mean bias with respect to the blood pressure cuffsensor, and so multiple measurements of both the PPG sensor and bloodpressure cuff would be averaged to the same mean value. However, thecuff is clearly more precise (shows higher precision) than does the PPGsensor. It should be noted that for the sensor associated with thepresentation 40 of FIG. 4, it is clear that the diastolic blood pressureestimate is more precise (i.e., has a tighter distribution) than is thesystolic blood pressure estimate. However, in practice, because therange of healthy diastolic blood pressure values is smaller than forthat of the systolic blood pressure, it may be more important to have asmaller a (a higher precision) for the diastolic readings than for thesystolic readings.

FIG. 5 illustrates an alternative presentation 50 to the presentation 40of FIG. 4. The presentation 50 may be via a display of a mobileelectronic device, for example, such as display 98 of client device 96in FIG. 8. The presentation 50 of FIG. 5 illustrates how an applicationmay present biometric information (in this case blood pressureinformation) to a user such that the user and/or someone monitoring theuser can assess the accuracy or urgency of the biometric assessment(health assessment, fitness assessment, etc.), according to otherembodiments of the present invention. The presentation 50 of FIG. 5displays the same data used in FIG. 4, but the probability distributionsare not shown. Rather, the 95% LOA are presented in shading such thatthe viewer understands the range of where the user's true blood pressure(or expected value) would likely be found, based on this singlemeasurement. The information may be shown as a distribution, fading fromthe center (e.g., 75 mmHG) with color shading density representing theprobability, e.g., shown as a Gaussian blur. Alternatively, theinformation may be shown as a range, without portending to a probabilitydistribution, as shown for the systolic information in FIG. 5.

It should be noted that the statistical characteristics of a sensor maynot always be Gaussian (normal) distributions, and in such case thepresented probability distribution should reflect the true nature ofthat sensor. A variety of statistical distributions are well known bythose skilled in the art, such as Poisson, Boltzmann, Bernoulli, and thelike, and may be utilized in accordance with embodiments of the presentinvention.

In some embodiments of the present invention, an estimation of abiometric parameter, based on sensor information collected by a mobileapplication, may be dependent on static or quasi-static biometricinformation. FIG. 6 illustrates a presentation 60 of hydration statusinformation (such as blood hydration information), in terms ofosmolality of a user, according to some embodiments of the presentinvention. The presentation 60 may be via a display of a mobileelectronic device, for example, such as display 98 of client device 96in FIG. 8. In the illustrated embodiment, it is known that theparticular PPG-based osmolality sensor underestimates osmolality andreduces precision for users who are taking “medication X”. In thisparticular case, since it is also known that the PPG-based osmolalitysensor estimates osmolality with a lower accuracy and precision underthe use of medication X, the mobile application is able to present thisinformation to the viewer by processing data collected from the sensor.In this particular case, the information is presented as two separateplots for the cases where the user is using and is not using “medicationX”. It is important to emphasize that without this statistical datapresented to the user or someone monitoring for the user, the ability toprescribe the appropriate therapy, treatment, or dosage oftherapy/treatment “X” may be substantially hindered. For example,without such information, a medication “X” dosage designed to lower aphysiological metric (heart rate, blood flow, blood hydration, bloodoxygen, blood pressure, etc.) by Y % may be mistakenly applied tosomeone who should have that metric lowered by only a fraction of Y %.

According to some embodiments of the present invention, data may bepresented to an app as a serial data stream containing not only themetrics measured by a sensor, but also information about the sensor dataintegrity and the sensor measurement statistics. For example, a serialdata stream 70 is illustrated in FIG. 7A in which a metric 72,associated metric integrity 74, and metric statistic 76 are reported foreach sensor measurement. Regular updates of metric statistics 76 may beimportant when statistics are changing dynamically. For example, if asensor or associated processor detects an environmental condition (i.e.,indoor or outdoor temperature condition, indoor or outdoor humiditycondition, etc.), physical condition (i.e., age, blood perfusion,metabolic state, elevated biometric parameter, etc.), or physicalactivity (i.e., rest, exercise, shivering, shaking, controlledbreathing, etc.) of the user that is known to change metric statistics,then these metric statistics may be changed dynamically in time witheach sensor measurement to account for these changes in conditions. Soas a particular example, if a wearable device comprises both anenvironmental sensor—in this case for measuring ambient temperature—anda physiological sensor—in this case a tympanic temperature sensor—and ifit is known that the tympanic temperature sensor readings become lessaccurate with higher ambient temperature, then the reported metricstatistics associated with tympanic temperature sensor may be updated toaccount for the lower accuracy.

According to other embodiments as illustrated in FIG. 7B, a serial datastream 80 of metrics 82 and associated metric integrity 84 includesmetric statistics 86 that are provided during the beginning of streamingdata transmission only. This configuration may be useful when metricstatistics are essentially static and do not change substantially withcontinued measurements.

In a specific example of the embodiment of FIG. 7B, consider a PPGsensor configured to sense blood flow information and body motioninformation. The PPG sensor in this example includes at least oneoptical emitter, at least one optical detector, and at least one motionsensor. Such a sensor is able to sense scattered light from the body ofa subject, as well as body motion, and may also be configured to processthat information, via a processor in communication with the sensorelements, into various metrics, such as heart rate, breathing rate,blood pressure, blood analyte levels, blood oxygenation levels, RRi,HRV, cadence, speed, jumping height, exercise frequency, and the like,as described in U.S. Pat. No. 8,700,111. A serial data stream from a PPGsensor, such as serial data stream 80 of FIG. 7B, may provideinformation not only about one or more of these metrics, but also aboutthe data integrity of each metric and the statistics of each metric. Inthis way, a mobile app can present sensor data as shown in FIGS. 2-6 toa user, for example, via a display 98 of a client device 96 (FIG. 8). Inthe case of this particular PPG sensor, assuming it is enabled withactive noise removal of noise artifacts from motion and the environment(as described in U.S. Pat. No. 8,888,701, which is incorporated hereinby reference in its entirety), the sensor statistics may be essentiallystatic throughout various conditions, and thus the metric statistics maybe reported just once (represented by block 86) at the beginning ofcommunication with a client device 96 (FIG. 8).

In a specific example of the embodiment of FIG. 7A, consider abioimpedance sensor configured to sense impedance changes along the skinof a person during exercise. As the person exercises, the person'sconditions may change, i.e., skin conductivity may change rapidly, dueto sweat, skin temperature, and other factors. Each condition may affectthe metric statistics. If the bioimpedance sensor, another sensor, or anassociated processor is configured to measure such conditions, then theserial data stream, such as serial data stream 70 of FIG. 7A, may bemodified to account for these dynamically changing statistics. As aspecific example, an associated processor may process 1) skin sweat orskin hydration information from a skin sweat sensor or skin hydrationsensor and 2) skin impedance from a bioimpedance sensor, such that themetric statistics of the bioimpedance sensor are updated based on theskin sweat or skin hydration values. The processing may compriseprocessing a function (linear or nonlinear), of these sensor inputs withmetric statistics as the output. Alternatively, the processing mayutilize a look-up table between the sensor inputs and the metricstatistics. Other processing methodologies may be used, as well.

FIG. 8 illustrates a system 90 for implementing aspects of the presentinvention, and includes one or more sensor elements 92 configured tosense information (i.e., physiological and/or physical activityinformation about a subject; environmental information in a vicinity ofthe subject, etc.), one or more processors 94 configured to processsensor information and communicate processed information to a clientdevice 96. The client device 96 is configured to receive processedinformation (such as data streams 70 or 80) and to make this informationavailable for a person or device, for example via a display 98 of theclient device 96. To understand and utilize the processed data from thesensor system 90, the client 96 may utilize a communication protocol,such as an application programming interface (API), associated with thesensor system 90. Utilizing an API may help the sensor system scale withmultiple client(s) 96.

The arrows are drawn bi-directionally as the processor(s) 94 maycommunicate information to the sensor element(s) 92 (such as changingsensor polling, gain, actuation elements, etc.) and the client device 96may send commands to reconfigure the processor(s) 94 (such as modifyingprocessing algorithms or communication protocols). The dotted line 97around the sensor element(s) 92 and processor(s) 94 represents the caseof a “smart sensor”, wherein the smart sensor 97 comprises both a sensorelement 92, processor 94 and memory 95. For example, a smartphotodetector for PPG monitoring may comprise a photodiode sensorelement 92 and an associated processor 94 to actively bias thephotodiode, control parameters of the associated optical emitters,control analog-to-digital conversion, actively remove motion artifactsand environmental artifacts via a noise reference and active filter(such as an adaptive filter or the like), or the like. However,embodiments of the present invention are not limited to an integratedsmart sensor.

For example, a blood pressure sensor 92 may be combined with a perfusionsensor 92 to communicate with a processor 94. The processor may beconfigured to communicate a data stream to a client device 96. Forexample, a serial data stream 70 as shown in FIG. 7A may be communicatedto a client device 96. Information from the perfusion sensor 92 may beprocessed by the processor 94 to update the metric statistics of theblood pressure sensor 92, as these may be changing dynamically in time.In some cases, the client device 96 may send a command to the processor94 to change the processing characteristics. For example, if the clientdevice 96 receives a message that the user is taking a medication, thismay cause the processor 94 to change the metric statistics of theresulting data-stream such that the data communication is more accurate.

It should be noted that although serial configurations of data flow havebeen shown and may be more beneficial in practice, elements of thepresent invention may be used for parallel data transmission, where datafrom multiple sensors 92 is streamed in parallel rather than serially.In such case, the communication protocol (i.e., API) associated with thesensor system 90 may be configured for parallel data communication.

The system 90 of FIG. 8 may also be used for research or autonomouscalibration purposes. For example, biometric sensor inputs may beprocessed to determine the metric integrity and metric statisticsassociated with the sensor 92. For example, the standard deviation ofsensor measurements can be compared against a known relationship of abenchmark sensor, and then metric statistics can be updated for thatsensor 92 based on these findings. Namely, there may be a relationshipbetween the raw measurement statistics of a series of measurements(i.e., the standard deviation and/or mean of a plurality of sensormeasurements) and the metric statistics (the standard deviation and/ormean of the differences between the sensor readings and benchmarkreadings), and the metric statistics may be updated based on changes inthe measurement statistics.

Alternatively, a sensor 92 and an associated benchmark sensor maygenerate data for processing that may be used to judge the metricstatistics of the sensor 92 with respect to the benchmark, and themetric statistics may be autonomously updated accordingly. In such aconfiguration, by processing sensor data and benchmark data over aperiod of time, a relationship may be learned between the metricintegrity and metric statistics of a sensor 92. This information canthen be updated in future processing of sensor data, such thatdynamically changing metric integrity values may be processed intodynamically changing metric statistics values (FIG. 7A) in real-time.

For example, consider the processor(s) 94 of system 90 in which a firstalgorithm is used thereby to extract a signal from noise present in thesensor inputs and a second algorithm is used thereby to process thissignal directly into a measurement to be reported. The first algorithmcould measure the signal-to-noise (S/N) ratio of a metric and thenexpress the S/N ratio as the metric integrity. In this example, alaboratory study may reveal that the sensor reported metric follows anormal distribution around the benchmark. This normal distribution couldbe reportable as metric statistics.

As another example, consider the case where a smart sensor 97 asdescribed with respect to FIG. 8 (sensor element 92+processor 94+memory95), is able to determine the number of valid measurements collectedduring a metric sampling period and is also able to relate a metricstatistic/integrity that is dependent on the number of valid samplemeasurements, wherein the valid sample measurements comprise thosesensor measurements characterized by a sufficiently high signal-to-noise(S/N) ratio. As a specific example, a PPG-based smart sensor 97 may beused to assess a metric, such as blood pressure, blood flow, bloodhydration, blood analyte (bilirubin, hemoglobin, electrolyte, etc.)level, and the like. In such case, a plurality of PPG waveforms may beprocessed together to assess a single metric, but some waveforms may benoisy (due to motion noise or electrical noise, for example), and themetric statistics or metric integrity for that metric may be dependenton the number of valid waveforms used to process that metric. Forexample the standard deviation of the metric statistics may decrease(improve) with an increasing number of valid waveforms (for the metricsampling period) or an increasing ratio of valid waveforms to invalidwaveforms. In such case, the algorithm on the processor used to processthe metric may comprise a way of determining which PPG waveforms arevalid, counting the number of valid waveforms, and comparing the validnumber of waveforms with a relationship stored in memory 95 relating thenumber of valid waveforms to metric statistics and/or metric integrity.The processor 94 may then report the appropriate metric statistics andintegrity to a client device 96 (based on the number of validwaveforms). As a specific example of determining whether a PPG waveformis valid, a processor 94 may process a waveform to determine thewaveform's amplitude and/or slope, and if that waveform's amplitudeand/or slope is outside of a desired range, the processor 94 may deemthat waveform invalid.

FIG. 9 illustrates a presentation 100 of data to a user or someonemonitoring a user via a display 98 of a client device 96, such as asmartphone, by a sensor system 90 configured to generate a diagnostic(yes/no) assessment of a health condition, according to some embodimentsof the present invention. In this example, sensitivity/specificitycharacteristics have been included in the data stream (e.g., data stream70, 80 of FIGS. 7A-7B). As such, the application on the client device 96can present not only the diagnosis (the metric) of arrhythmia but alsothe metric statistics associated with the arrhythmia diagnosis via thesensor system 90, such as the 87% accuracy, the 20% false positive rate,and the 10% false negative rate associated with the sensor system 90diagnosing arrhythmia. This information can be useful in determining howto triage next steps for the user. In this case, a processor 94associated with the sensor system 90 communicates next steps to the useror person monitoring the user (i.e., it communicates that the personshould take the measurement 2 more times). This type of recommendationmay be included in the data stream itself (e.g., data stream 70, 80 ofFIGS. 7A-7B), characterized by the sensor properties themselves, or maybe processed by a processor, such as a processor associated with theclient device 96, via an algorithm configured to process metricstatistics information into a recommendation. In this example, followingthe third test, the user or person monitoring the user may be promptedwith an overall assessment or further next steps. The displaymethodology of FIG. 9 can be extremely useful in facilitating anautonomous diagnosis while also preventing overreactions (orunderreactions) from false positives and false negatives by providingstatistical context to the user or someone monitoring the user.

Though many examples of the invention described herein have referencedbiometric sensors for physiological monitoring, it should be noted thatvoice sensors may also be used, according to embodiments of the presentinvention. For example, a voice sensor may be known to have a certainstatistical probability of identifying voice information—such asidentifying words, phrases, a person's biometric identification, and thelike. Thus, a voice sensor 92 in communication with a client device 96may send a serial data stream of information comprising the voice IDinformation (i.e., the metric), the probability that the voice IDinformation is correct (i.e., the metric statistic), and the quality ofthe voice information itself (i.e., the metric integrity). The clientdevice 96 may then determine, based on all of this information, whetheror not to execute the voice information. As a specific example, theclient device 96 may collect serial data stream information from thevoice sensor 92 for the purpose of executing the voice command “turn on”(i.e., the “sensor metric” is “turn on”). But the client device 96 mayalso have information that the decibel level was very low (i.e., the“metric integrity” was very low) and that the particular voice sensor 92has a command transcription error (“metric statistic” error) of 25%.Thus, the processor in the client device 96 may process all thisinformation to determine that the voice command “turn on” should not beexecuted; the client device 96 may then visually show the user viadisplay 98 that the command “turn on” may have been requested by thevoice sensor 92 but was not executed by the client device 96 due tounsatisfactory sensor integrity and sensor statistics.

According to other embodiments of the present invention, acommunications protocol that is capable of transferring information froma physiological sensor 92 to a client device 96 is provided. In thisprotocol, events described below may occur as appropriate for thecommunications medium. For instance, event instances can be polled froma sensor 92 by a client device 96, or sent at regular intervals (e.g.,set by the sensor 92 or by the client device 96 or by convention), orsent asynchronously as measurement data becomes available or changes.

According to some embodiments of the present invention, thecommunication protocol is presented as a series of SensorEvents, such asthat shown in FIG. 10. A SensorEvent 200 may contain a Timestamp and oneor more SensorEventField 202. Each SensorEventField 202 is one of thefollowing: a MeasurementValue (one or more scalar numbers, depending onsensor) 204, a MeasurementIntegrity (scalar) 206, or aMeasurementStatistic 208 (described below). It should be understood thatvariations of FIG. 10 are permissible under embodiments of the presentinvention, and FIG. 10 represents just one potential embodiment.

For sensors 92 where a laboratory study determines that the measurementstatistics do not change (FIG. 7B), a SensorEvent 200 containing one ormore MeasurementStatistic 208 can be sent upon start up (i.e., sensorstart up), upon connection, periodically, or even with everyMeasurementValue 204. Other sensors 92 could take advantage of theprotocol supporting all fields changing dynamically and being updatedper SensorEvent 200. The MeasurementStatistic 208, described below, isrich enough to describe or approximate the statistical complexity ofbiological and natural processes, yet compact enough to be practical fortransmission by battery powered devices.

The MeasurementStatistic 208 may consist of the following:FunctionSelector 210, and one or more FunctionParameters 212. TheFunctionSelector 210 is a selection from statistical distributionfunctions, some of which may be well-known functions, or from a specialvalue indicating a series of points for curve fitting (to allow evengreater flexibility). Non-limiting examples include NormalDistribution(Gaussian), CurveFitSeries, ROC (Receiver Operating Characteristic)Curves, and BinomialDistribution. However, it is understood that otherstatistical distribution curves may be utilized in accordance withembodiments of the present invention.

Table 1 below illustrates each appropriate FunctionParameter 212 foreach FunctionSelector 210.

TABLE 1 FunctionSelector FunctionParameters NormalDistribution μ, σCurveFitSeries Num point pairs (n), x₀, p₀, x₁, p₁, . . . x_(n), p_(n)BinomialDistribution N, p

An advantage of embodiments of the present invention is that virtuallyany sensor of a given metric (e.g., physiological, environmental, etc.)can be used by a client device to generate an assessment for a subjector group of subjects, with contextual statistical information as aguide. For example, if heart rate readings are utilized by a clientdevice to estimate caloric metabolism, the accuracy of this model, basedon heart rate, can be presented to the user in context with thestatistics/integrity of the heart rate values. Moreover, if ECGinformation is utilized by a client device to assess a cardiac condition(such as a heart attack, arrhythmia, atrial fibrillation, and the like),then the likelihood that that condition exists (or does not exist) canbe presented to the subject(s) or someone monitoring the subject(s) incontext of the statistics/integrity of the ECG values. This isparticularly valuable for a marketplace where there may be numerousvendors offering the same types of sensors, but with each sensor havingdifferent metric integrity and/or metric statistics characteristics, tobe used with client devices in generating multiparametric physiologicalassessments. Indeed, the invention promotes accuracy and precision in acompetitive marketplace, because competing sensors can be differentiatedin the marketplace based on the accuracy and precision of theirmeasurements as determined by prior clinical studies for that particularsensor, where the prior clinical studies have determined the metricintegrity and/or metric statistics for that sensor. In some use cases,the greatest accuracy and precision may be critical. But in others, morecost-effective sensors, which may not exhibit the ideal accuracy and/orprecision, may be “good enough”, depending the on the diagnoses made andthe expense or risks of the resulting therapy. Because the metricstatistics and metric integrity of sensor data required to diagnose andtreat various health conditions is often known way in advance of amedical product launch, in part due to regulatory requirements, theproposed invention may provide full visibility to the marketplace topick and choose “good enough” sensors based on price. Thus, theinvention supports positive market forces to drive down costs and alsoto promote safety in medical products. Additionally, with embodiments ofthe present invention, the client software for each sensor may not needto be rewritten for each sensor offered by the vendors, as the clientassessments can always be generated and reviewed in context of sensorstatistics.

Example embodiments are described herein with reference to blockdiagrams and flowchart illustrations. It is understood that a block ofthe block diagrams and flowchart illustrations, and combinations ofblocks in the block diagrams and flowchart illustrations, can beimplemented by computer program instructions that are performed by oneor more computer circuits. These computer program instructions may beprovided to a processor circuit of a general purpose computer circuit,special purpose computer circuit, and/or other programmable dataprocessing circuit to produce a machine, such that the instructions,which execute via the processor circuit and/or other programmable dataprocessing apparatus, transform and control transistors, values storedin memory locations, and other hardware components within such circuitryto implement the functions/acts specified in the block diagrams andflowchart block or blocks, and thereby create means (functionality)and/or structure for implementing the functions/acts specified in theblock diagrams and flowchart blocks.

These computer program instructions may also be stored in a tangiblecomputer-readable medium that can direct a client device or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instructions whichimplement the functions/acts specified in the block diagrams andflowchart blocks.

A tangible, non-transitory computer-readable medium may include anelectronic, magnetic, optical, electromagnetic, or semiconductor datastorage system, apparatus, or device. More specific examples of thecomputer-readable medium would include the following: a portablecomputer diskette, a random access memory (RAM) circuit, a read-onlymemory (ROM) circuit, an erasable programmable read-only memory (EPROMor Flash memory) circuit, a portable compact disc read-only memory(CD-ROM), and a portable digital video disc read-only memory(DVD/BlueRay).

The computer program instructions may also be loaded onto a clientdevice and/or other programmable data processing apparatus to cause aseries of operational steps to be performed on the client device and/orother programmable apparatus to produce a computer-implemented processsuch that the instructions which execute on the client device or otherprogrammable apparatus provide steps for implementing the functions/actsspecified in the block diagrams and flowchart blocks. Accordingly,embodiments of the present invention may be embodied in hardware and/orin software (including firmware, resident software, micro-code, etc.)that runs on a processor such as a digital signal processor, which maycollectively be referred to as “circuitry,” “a module” or variantsthereof.

It should also be noted that in some alternate implementations, thefunctions/acts noted in the blocks may occur out of the order noted inthe flowcharts. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved. Moreover, the functionality of a given block of the flowchartsand block diagrams may be separated into multiple blocks and/or thefunctionality of two or more blocks of the flowcharts and block diagramsmay be at least partially integrated. Finally, other blocks may beadded/inserted between the blocks that are illustrated. Moreover,although some of the diagrams include arrows on communication paths toshow a primary direction of communication, it is to be understood thatcommunication may occur in the opposite direction to the depictedarrows.

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. The invention is defined by the following claims, withequivalents of the claims to be included therein.

That which is claimed is:
 1. A method of presenting data from a remotesensor that is monitoring a subject and/or an environment in a vicinityof the subject, the method comprising the following performed by aclient device: obtaining a data stream from the remote sensor via acommunication protocol, wherein the data stream comprises a sensormetric, a metric identifier, and dynamically updated integrityinformation about an accuracy of the sensor metric; identifying astatistical distribution function associated with the remote sensor viaa function selector provided as part of the communication protocol; anddisplaying the sensor metric with statistical information about thesensor metric using the identified statistical distribution function viaa display associated with the client device.
 2. The method of claim 1,wherein the communication protocol is an application programminginterface associated with the remote sensor.
 3. The method of claim 1,wherein the sensor metric statistical information comprises informationabout accuracy of the remote sensor.
 4. The method of claim 1, whereinthe sensor metric statistical information is displayed as at least oneprobability distribution curve.
 5. The method of claim 1, wherein thedata stream further comprises a diagnostic assessment of a healthcondition of the subject, and wherein the method further comprisesdisplaying the diagnostic assessment of the health condition of thesubject via the display.
 6. The method of claim 1, wherein the datastream is obtained wirelessly from the remote sensor.
 7. The method ofclaim 1, wherein the sensor metric statistical information is obtainedfrom the remote sensor.
 8. The method of claim 1, wherein the clientdevice is a mobile communication device.
 9. The method of claim 1,wherein the remote sensor is a photoplethysmography (PPG) sensor or ablood pressure sensor.
 10. A client device, comprising: a display; andat least one processor configured to: obtain a data stream from a remotesensor via a communication protocol, wherein the remote sensor is aphysiological sensor monitoring a subject and/or an environmental sensormonitoring an environment in a vicinity of the subject, wherein the datastream comprises a sensor metric, a metric identifier, and dynamicallyupdated integrity information about an accuracy of the sensor metric;identify a statistical distribution function associated with the remotesensor via a function selector provided as part of the communicationprotocol; and display, via the display, the sensor metric withstatistical information about the sensor metric using the identifiedstatistical distribution function.
 11. The client device of claim 10,wherein the communication protocol is an application programminginterface associated with the remote sensor.
 12. The client device ofclaim 10, wherein the at least one processor is configured tocommunicate with one or more processors associated with the remotesensor and to modify a processing algorithm of the one or moreprocessors.
 13. The client device of claim 10, wherein the at least oneprocessor is configured to communicate with one or more processorsassociated with the remote sensor and to modify a communication protocolof the one or more processors.
 14. The client device of claim 10,wherein the data stream further comprises a diagnostic assessment of ahealth condition of the subject, and wherein the at least one processoris further configured to display the diagnostic assessment of the healthcondition of the subject via the display.
 15. The client device of claim10, wherein the sensor metric statistical information comprisesinformation about accuracy of the remote sensor.
 16. The client deviceof claim 10, wherein the at least one processor displays the sensormetric statistical information as at least one probability distributioncurve.
 17. The client device of claim 10, wherein the at least oneprocessor obtains the data stream wirelessly from the remote sensor. 18.The client device of claim 10, wherein the client device is a mobilecommunication device.
 19. The client device of claim 10, wherein theremote sensor is a photoplethysmography (PPG) sensor or a blood pressuresensor.
 20. The client device of claim 10, wherein the remote sensorfurther comprises a voice sensor, and wherein the at least one processoris further configured to process voice commands received from the voicesensor.